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Good news of the front of restoring UCLA's grants! calmatters.org/education/20...
Thrilled that my paper is out in the @nature.com. We explored how the brain builds complex tasks by compositionally combining simpler sub-task representations. The brain flexibly performs multiple tasks by dynamically reusing neural subspaces for sensory inputs and motor actions rdcu.be/eRVUk
🎇 Excited to finally share JL Romero Sosa’s publication! Results are from single-cell imaging in different subregions of rat frontal cortex during ✨de novo learning. Spoiler: everything is not everywhere all at once www.nature.com/articles/s41...
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Nature - The brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations.
rdcu.be
The grants were terminated over alleged DEI violations. University of California attorneys argued the actions were arbitrary and capricious.
calmatters.org
Building compositional tasks with shared neural subspaces
Federal judge orders Trump administration to restore hundreds of UCLA research grants
Neural coding of choice and outcome are modulated by uncertainty in orbitofrontal but not secondary motor cortex - Nature Communications
Neural mechanisms underlying flexible learning and decision-making are not fully understood. Using single-cell calcium imaging, authors here found that neurons in orbitofrontal and secondary motor cortex exhibit complementary roles in reward learning, with neurons in the former exerting a sustained role in conditions of uncertainty.
www.nature.com
We review studies showing that when brain areas face similar computational demands in social and non-social context, they perform the same computations. We argue that exaptation (repurposing of traits for new functions) played a key role in brain evolution.
Happy to share my new paper published in @nathumbehav.nature.com: A critical look at statistical power in computational modeling studies, particularly those based on model selection. www.nature.com/articles/s41...
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I argue that we need to account for the size of the model space when determining sample size, as larger model spaces reduce power. I also show that the commonly used “fixed effects” model selection approach is statistically unreliable. An analysis of the literature suggests shortcomings in both